A Probing-Based Explanation of the Effect of Existential Presuppositions on Hallucinations in Large Language Models

Vaquet J, Pütz O, Paaßen B (2026)
In: Findings of the Association for Computational Linguistics: EMNLP 2026. Lee H-Y, Dev S, Li J, Elliott D (Eds); Association for Computational Linguistics: 519-535.

Realistic Benchmarks for Fair Stream Learning

Lammers K, Vaquet V, Vaquet J, Hammer B (2026)
In: Neural Information Processing. 32nd International Conference, ICONIP 2025, Okinawa, Japan, November 20–24, 2025, Proceedings, Part III. Taniguchi T, Leung CSA, Kozuno T, Yoshimoto J, Mahmud M, Doborjeh M, Doya K (Eds); Communications in Computer and Information Science. Singapore: Springer Nature Singapore: 169-184.

Realistic Benchmarks for Fair Stream Learning

Lammers K, Vaquet V, Vaquet J, Hammer B (Submitted) .

Compression-based $k$NN for Class Incremental Continual Learning

Vaquet V, Vaquet J, Hinder F, Hammer B (2025)
In: ESANN 2025 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 45-50.

Energy Efficient Online Stream Classification under Concept Drift on FPGAs for Edge Computing

Vaquet J, Porrmann F, Pilz S, Vaquet V, Hagemeyer J, Rückert U, Hammer B (2025)
In: 2025 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

Battle of Water Demand Forecasting

Alvisi S, Franchini M, Marsili V, Mazzoni F, Salomons E, Housh M, Abokifa A, Arsova K, Ayyash F, Bae H, Barreira R, et al. (2025)
Journal of Water Resources Planning and Management 151(10).

Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks

Vaquet V, Hinder F, Artelt A, Ashraf I, Strotherm J, Vaquet J, Brinkrolf J, Hammer B (2024)
In: Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Wand M, Malinovská K, Schmidhuber J, Tetko IV (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 155-170.

Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations

Vaquet V, Hinder F, Vaquet J, Lammers K, Quakernack L, Hammer B (2024)
In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

Self-Supervised Learning from Incrementally Drifting Data Streams

Vaquet V, Vaquet J, Hinder F, Malialis K, Panayiotou C, Polycarpou M, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 431-436.

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

Zanutto D, Michalopoulos C, Chatzistefanou G-A, Vamvakeridou-Lyroudia L, Tsiami L, Glynis K, Samartzis P, Hermes L, Hinder F, Vaquet J, Vaquet V, et al. (2024)
In: The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024). Basel Switzerland: MDPI: 60.

Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings

Vaquet V, Hinder F, Vaquet J, Brinkrolf J, Hammer B (2021)
IEEE Symposium Series on Computational Intelligence: 1-7.